Because int and not InstSeqNum was used in a couple of places, you can
overflow the int type and thus get wierd bugs when the sequence number
is negative (or some wierd value)
remove constructors that werent being used (it just gets confusing)
use initialization list for all the variables instead of relying on initVars()
function
-use a pointer to CacheReqPacket instead of PacketPtr so correct destructors
get called on packet deletion
- make sure to delete the packet if the cache blocks the sendTiming request
or for some reason we dont use the packet
- dont overwrite memory requests since in the worst case an instruction will
be replaying a request so no need to keep allocating a new request
- we dont use retryPkt so delete it
- fetch code was split out already, so just assert that this is a memory
reference inst. and that the staticInst is available
If there is an outstanding table walk and no other activity in the CPU
it can go to sleep and never wake up. This change makes the instruction
queue always active if the CPU is waiting for a store to translate.
If Gabe changes the way this code works then the below should be removed
as indicated by the todo.
We only support EABI binaries, so there is no reason to support OABI syscalls.
The loader detects OABI calls and fatal() so there is no reason to even check
here.
makeArmSystem creates both bare-metal and Linux systems more cleanly.
machine_type was never optional though listed as an optional argument; a system
such as "RealView_PBX" must now be explicitly specified. Now that it is a
required argument, the placement of the arguments has changed slightly
requiring some changes to calls that create ARM systems.
The ARM performance counters are not currently supported by the model.
This patch interprets a 'reset performance counters' command to mean 'reset
the simulator statistics' instead.
It's confusing (especially to new users), when you are setting some standard
parameters (as defined in Options.py) and they aren't reflected in the simulations
so we might as well link the settings in CacheConfig.py to those in Options.py
"executing" isnt a very descriptive debug message and in going through the
output you get multiple messages that say "executing" but nothing to help
you parse through the code/execution.
So instead, at least print out the name of the action that is taking
place in these functions.
Overall, continue to progress Ruby debug messages to more of the normal M5
debug message style
- add a name() to the Ruby Throttle & PerfectSwitch objects so that the debug output
isn't littered w/"global:" everywhere.
- clean up messages that print over multiple lines when possible
- clean up duplicate prints in the message buffer
In certain actions of the L1 cache controller, while creating an outgoing
message, the machine type was not being set. This results in a
segmentation fault when trace is collected. Joseph Pusudesris provided
his patch for fixing this issue.
previous changesets took a closer look at memory mgmt in the inorder model and sought to avoid
dynamic memory mgmt (for access to pipeline resources) as much as possible. For the regressions
that were run, the sims are about 2x speedup from changeset 7726 which is the last change
since the recent commits in Feb. (note: these regressions now are 4-issue CPUs instead of just 1-issue)
keep track of when an instruction needs the execution
behind it to be serialized. Without this, in SE Mode
instructions can execute behind a system call exit().
resources don't need to call getLatency because the latency is already a member
in the class. If there is some type of special case where different instructions
impose a different latency inside a resource then we can revisit this and
add getLatency() back in
each resource has a certain # of requests it can take per cycle. update the #s here
to be more realistic based off of the pipeline width and if the resource needs to
be accessed on multiple cycles
---
need to delete the cache request's data on clearRequest() now that we are recycling
requests
---
fetch unit needs to deallocate the fetch buffer blocks when they are replaced or
squashed.
formerly, to free up bandwidth in a resource, we could just change the pointer in that resource
but at the same time the pipeline stages had visibility to see what happened to a resource request.
Now that we are recycling these requests (to avoid too much dynamic allocation), we can't throw
away the request too early or the pipeline stage gets bad information. Instead, mark when a request
is done with the resource all together and then let the pipeline stage call back to the resource
that it's time to free up the bandwidth for more instructions
*** inteface notes ***
- When an instruction completes and is done in a resource for that cycle, call done()
- When an instruction fails and is done with a resource for that cycle, call done(false)
- When an instruction completes, but isnt finished with a resource, call completed()
- When an instruction fails, but isnt finished with a resource, call completed(false)
* * *
inorder: tlbmiss wakeup bug fix
take away all instances of reqMap in the code and make all references use the built-in
request vectors inside of each resource. The request map was dynamically allocating
a request per instruction. The request vector just allocates N number of requests
during instantiation and then the surrounding code is fixed up to reuse those N requests
***
setRequest() and clearRequest() are the new accessors needed to define a new
request in a resource
we are going to be getting away from creating new resource requests for every
instruction so no more need to keep track of a reqRemoveList and clean it up
every tick
first change in an optimization that will stop InOrder from allocating new memory for every instruction's
request to a resource. This gets expensive since every instruction needs to access ~10 requests before
graduation. Instead, the plan is to allocate just enough resource request objects to satisfy each resource's
bandwidth (e.g. the execution unit would need to allocate 3 resource request objects for a 1-issue pipeline
since on any given cycle it could have 2 read requests and 1 write request) and then let the instructions
contend and reuse those allocated requests. The end result is a smaller memory footprint for the InOrder model
and increased simulation performance
